STUDENT ACADEMIC PERFORMANCE PREDICTION SYSTEM USING ENSEMBLE ALGORITHM

Abstract

Predicting student academic performance is crucial for enhancing educational outcomes and supporting timely interventions. There has been an increased interest in creating precise models for projecting student performance as a result of the introduction of machine learning techniques and the accessibility of large-scale educational data. However, the creation of predictive models in educational contexts is frequently hampered by the sensitive nature of student data and the requirement to uphold privacy and data security. This study develops a student Academic performance prediction system that leverages ensemble techniques, specifically focusing on Random Forest and other robust machine learning methods. By analyzing data such as demographic attributes, behavioral factors, and prior academic records, the system identifies patterns that influence final grades, categorizing students' performance levels. Categorical data is encoded, and a test-train split methodology is employed to assess the model's predictive accuracy. The Random Forest Regressor is particularly effective in capturing complex patterns by combining multiple decision trees, resulting in a high degree of accuracy and reduced overfitting. The model’s effectiveness is measured by mean squared error (MSE) scores, indicating its ability to deliver precise predictions. Additionally, the system stores trained models and encoding schemes, facilitating real-time usage and scalability for larger datasets. This prediction system offers educators actionable insights for academic support, enhances individualized student guidance, and enables targeted educational strategies. Overall, the application of ensemble techniques in student performance prediction presents a valuable tool for data-driven decision-making in educational settings.